In this thesis, we explored the automated detection and classification of ultrasonic vocalizations (USVs) in rats. We analyzed established methods such as MUPET, DeepSqueak, and USVSEG and tested them on the dataset from the Institute of Pathophysiology at the Faculty of Medicine, University of Ljubljana. We developed our own method based on fine-tuning the YOLOv11 model, which performs both detection and classification simultaneously. The results show that despite its simple design, our method is comparable to existing solutions and achieves an F1 score above 80 \% in classification. This work contributes to the advancement of automatic USV analysis and opens up opportunities for further research in the field.
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